---
title: "Homework 3 - Data Analysis with R"
author: "Your Name"
date: "Due: Friday, March 15, 2024"
format: html
execute:
  warning: false
  message: false
---

## Setup

Load the required packages for this assignment:

```{r setup}
library(tidyverse)
library(palmerpenguins)
```

## Exercise 1: Basic Data Exploration

Examine the `penguins` dataset from the `palmerpenguins` package. Your task is to create a summary of the dataset that shows the number of observations and variables, and identify any missing values.

```{r ex1-student}
# Write your code here to:
# 1. Display the dimensions of the penguins dataset
# 2. Show the structure of the dataset
# 3. Count missing values in each column

```

```{r ex1-key}
# Solution: Basic data exploration
# 1. Display dimensions
cat("Dataset dimensions:", dim(penguins), "\n")
cat("Rows:", nrow(penguins), "Columns:", ncol(penguins), "\n\n")

# 2. Show structure
str(penguins)

# 3. Count missing values
cat("\nMissing values by column:\n")
penguins %>%
  summarise(across(everything(), ~ sum(is.na(.))))
```

## Exercise 2: Data Visualization

Create a scatter plot showing the relationship between flipper length and body mass for penguins. Color the points by species and add appropriate labels and a title.

```{r ex2-student}
# Create a scatter plot with:
# - x-axis: flipper_length_mm
# - y-axis: body_mass_g
# - color by species
# - add appropriate labels and title

ggplot(data = penguins, aes(x = ___, y = ___)) +
  geom_point(aes(color = ___)) +
  labs(
    title = "___",
    x = "___",
    y = "___"
  )
```

```{r ex2-key}
# Solution: Scatter plot of flipper length vs body mass
ggplot(data = penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_point(aes(color = species), alpha = 0.8, size = 2) +
  labs(
    title = "Penguin Flipper Length vs Body Mass by Species",
    x = "Flipper Length (mm)",
    y = "Body Mass (g)",
    color = "Species"
  ) +
  theme_minimal() +
  scale_color_viridis_d()
```

## Exercise 3: Statistical Analysis

Calculate summary statistics for bill length by species. Create a table showing the mean, median, standard deviation, and count for each species.

```{r ex3-student}
# Calculate summary statistics for bill_length_mm by species
# Include: mean, median, standard deviation, and count
# Remove missing values before calculating

penguins %>%
  # Add your code here

```

```{r ex3-key}
# Solution: Summary statistics for bill length by species
penguins %>%
  filter(!is.na(bill_length_mm)) %>%
  group_by(species) %>%
  summarise(
    count = n(),
    mean_bill_length = round(mean(bill_length_mm), 2),
    median_bill_length = round(median(bill_length_mm), 2),
    sd_bill_length = round(sd(bill_length_mm), 2),
    .groups = "drop"
  ) %>%
  arrange(desc(mean_bill_length))
```

## Exercise 4: Advanced Data Manipulation

Filter the dataset to include only penguins with complete data (no missing values), then create a new variable called `bill_ratio` that represents the ratio of bill length to bill depth. Finally, identify which species has the highest average bill ratio.

```{r ex4-student}
# Step 1: Filter for complete cases
# Step 2: Create bill_ratio variable (bill_length_mm / bill_depth_mm)
# Step 3: Calculate average bill_ratio by species
# Step 4: Identify species with highest average ratio

```

```{r ex4-key}
# Solution: Advanced data manipulation
complete_penguins = penguins %>%
  # Remove rows with any missing values
  filter(complete.cases(.)) %>%
  # Create bill_ratio variable
  mutate(bill_ratio = bill_length_mm / bill_depth_mm)

# Calculate average bill ratio by species
bill_ratio_summary = complete_penguins %>%
  group_by(species) %>%
  summarise(
    avg_bill_ratio = round(mean(bill_ratio), 3),
    n = n(),
    .groups = "drop"
  ) %>%
  arrange(desc(avg_bill_ratio))

print(bill_ratio_summary)

# Identify species with highest average bill ratio
highest_ratio_species = bill_ratio_summary %>%
  slice_max(avg_bill_ratio, n = 1) %>%
  pull(species)

cat("\nSpecies with highest average bill ratio:", as.character(highest_ratio_species))
```

## Bonus Exercise: Conditional Logic

Write a function that categorizes penguins as "small", "medium", or "large" based on their body mass. Use the following criteria:
- Small: body mass < 3500g
- Medium: body mass between 3500g and 4500g  
- Large: body mass > 4500g

Apply this function to create a new column and create a summary table.

```{r bonus-student}
# Create a function to categorize penguins by size
categorize_size = function(mass) {
  # Add your conditional logic here
  
}

# Apply the function and create summary
```

```{r bonus-key}
# Solution: Conditional logic for size categorization
categorize_size = function(mass) {
  case_when(
    is.na(mass) ~ "Unknown",
    mass < 3500 ~ "Small",
    mass >= 3500 & mass <= 4500 ~ "Medium",
    mass > 4500 ~ "Large"
  )
}

# Apply the function and create summary
penguins_with_size = penguins %>%
  mutate(size_category = categorize_size(body_mass_g))

# Create summary table
size_summary = penguins_with_size %>%
  count(species, size_category) %>%
  pivot_wider(names_from = size_category, values_from = n, values_fill = 0)

print(size_summary)

# Overall size distribution
penguins_with_size %>%
  count(size_category) %>%
  mutate(percentage = round(n / sum(n) * 100, 1))
```

(hw1 = parse_qmd("hw1.qmd"))